Similar Scenes arouse Similar Emotions: Parallel Data Augmentation for Stylized Image Captioning

• URL: http://arxiv.org/abs/2108.11912v1
• Date: Thu, 26 Aug 2021 17:08:58 GMT
• Title: Similar Scenes arouse Similar Emotions: Parallel Data Augmentation for Stylized Image Captioning
• Authors: Guodun Li, Yuchen Zhai, Zehao Lin, Yin Zhang
• Abstract summary: Stylized image captioning systems aim to generate a caption consistent with a given style description. Many studies focus on unsupervised approaches, without considering from the perspective of data augmentation. We propose a novel Extract-Retrieve-Generate data augmentation framework to extract style phrases from small-scale stylized sentences.
• Score: 3.0415487485299373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stylized image captioning systems aim to generate a caption not only semantically related to a given image but also consistent with a given style description. One of the biggest challenges with this task is the lack of sufficient paired stylized data. Many studies focus on unsupervised approaches, without considering from the perspective of data augmentation. We begin with the observation that people may recall similar emotions when they are in similar scenes, and often express similar emotions with similar style phrases, which underpins our data augmentation idea. In this paper, we propose a novel Extract-Retrieve-Generate data augmentation framework to extract style phrases from small-scale stylized sentences and graft them to large-scale factual captions. First, we design the emotional signal extractor to extract style phrases from small-scale stylized sentences. Second, we construct the plugable multi-modal scene retriever to retrieve scenes represented with pairs of an image and its stylized caption, which are similar to the query image or caption in the large-scale factual data. In the end, based on the style phrases of similar scenes and the factual description of the current scene, we build the emotion-aware caption generator to generate fluent and diversified stylized captions for the current scene. Extensive experimental results show that our framework can alleviate the data scarcity problem effectively. It also significantly boosts the performance of several existing image captioning models in both supervised and unsupervised settings, which outperforms the state-of-the-art stylized image captioning methods in terms of both sentence relevance and stylishness by a substantial margin.

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